ABSTRACT Prematurity is the largest single cause of death in children under five in the world and lower respiratory tract infections (LRTI) are the top cause of hospitalization and mortality in premature infants. Clinical tools to predict and prevent severe LRTI in premature pediatric patients are critically needed to allow early interventions to decrease the high morbidity and mortality in this patient group. Although imaging biomarkers of lung disease from computed tomography have been successfully used in adults, they entail heightened risks for children due to cumulative radiation and the need for sedation. Our goal is to address these gaps and improve clinical practice by developing an objective imaging biomarker framework to assess the risk of severe respiratory disease in premature babies using non-invasive low-radiation X-ray imaging. Previous efforts lead by Dr. Linguraru (Principal Investigator) at Children's National Health System (CNHS) have focused on developing technical components to quantify lung structural data from chest X-ray (CXR) in children. The image processing pipeline developed at CNHS integrates three novel technical components: a) automatic lung segmentation, b) obtrusive object removal in CXR, and c) severity quantification of lung pathology. These innovations enabled the development of a quantitative imaging software technology to quantify Lung Air trapping and Irregular opacities Radiological analyzer (LungAIR). This Phase I project builds on the methodology developed in preliminary work. We will analyze a large database (n=200) of retrospective CXR images from premature infants with known clinical outcomes, and further develop and validate lung imaging biomarkers. In Specific Aim 1 we will design and implement a prototype of the software technology (LungAIR) for the localization and quantification of heterogeneous aeration patterns in each lung quadrant through quantitative imaging algorithms. A graphical user interface will also be developed for the simple clinical use of the technology. In Specific Aim 2 we will perform clinical outcome analysis to correlate these biomarkers with the severity of lung disease. We will also combine our new imaging biomarkers with clinical parameters to design and evaluate a predictive model of LRTI risk in the first year of life to optimize the clinical management and improve the outcomes for premature babies. In this project, CNHS partners with Kitware to translate our research into a clinical software application and lay the groundwork for further developments and clinical studies in Phase II. In summary, our approach will enable better clinical management of diseases of prematurity leading to novel diagnostic strategies to improve treatment and outcomes for the highly vulnerable population of premature infants.